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Academic human capital in preindustrial Europe rose and fell unevenly across regions and centuries, and the clearest long-run gap separated northern and southern Europe. A 2026 study by Matthew Curtis, David de la Croix, Filippo Manfredini, and Mara Vitale builds annual series from 1200 to 1793 by scoring the publications of university professors and academy members, then aggregating those scores by city, present-day country, and 18 historically informed macro-regions. The series measure a documented scholarly population. They do not measure literacy, school attendance, or the skills of ordinary people, and reading them correctly depends on keeping those limits in view.
What the series measures
The study is published in Explorations in Economic History, volume 101 (July 2026), article 101756. Its data start from the Repertorium Eruditorum Totius Europae (RETE), which records individual-level information on university professors and members of scientific academies. For the preindustrial period, these are among the few groups for which systematic person-level records survive across much of Europe.
On that base, the authors build a composite index of individual academic human capital. The index rests on publication outcomes and scholarly visibility, and it is then summed or averaged over time and space. A scholar’s score therefore reflects documented output and how far that output was recognised in later sources.
That construction has a built-in limitation, which the authors acknowledge. Parts of the scholar records draw on sources such as Wikipedia and VIAF, which are updated continuously. Publication survival and later fame also decide which scholars appear prominently. A well-known Parisian professor is more likely to be recorded than an equally productive provincial teacher whose work was lost. The index is therefore a measure of the academic population captured by these sources. It cannot be read as a measure of education across the whole population.
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How the data are organised
The authors release their series at three levels of aggregation. Each answers a different question, and the choice changes the picture.
| Aggregation level | What it groups | Best used for | Main caution |
|---|---|---|---|
| City | Scholarly activity located in a single urban centre | Locating where universities and academies clustered | Cities with several institutions can dominate their surrounding region |
| Present-day country | Activity grouped by modern national borders | Comparing with modern statistics and national histories | Borders did not exist in this form in 1200, and modern states may blur older regional differences |
| Historical macro-region (18 regions) | Areas defined by long-run institutional, linguistic, religious, and political commonalities | Analysing long-term regional divergence | Boundaries are not fixed across the full period, so regions are not perfectly comparable across centuries |
The macro-region framework is described in the 2025 working-paper version of the study, which the authors circulated before journal publication. The working paper is the source for the method details summarised here, while the journal article is the citable publication.
Four data types
At each level, the study reports four measures. Keeping them separate matters, because a region can score high on one and low on another.
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- Total university human capital: the summed academic human capital of professors attached to universities.
- Number of universities: a count of universities active in the unit, which says nothing about the size of their faculties.
- Additional human capital from academy-only affiliations: the human capital of members who belonged to an academy but not to a university.
- Number of academies: a count of academies active in the unit.
Scholars often held more than one affiliation. For these cases the authors use equal-share allocation rules, dividing a scholar’s value across the institutions concerned. Readers comparing a per-institution figure with a total should check which allocation convention applies to the series they are using.
Why the study uses historical regions
Modern countries are convenient, but they group together areas that had little in common in 1500 and separate areas that did. The macro-regions are designed to capture long-run commonalities in institutions, languages, religion, and politics that modern borders can hide. Occitania, for instance, is not a modern country, and a national series for France would merge it with regions that followed quite different academic trajectories.
The trade-off is that historical regions are less stable. Their boundaries shift with political change, so a macro-region in 1300 and the same name in 1700 may not cover identical territory. The authors treat boundaries as flexible over the full period and say so in the study.
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Why the series ends in 1793
The annual series stop in 1793. The authors identify the French Revolution as a decisive break in higher education. It abolished universities and academies in France, and the same policy was extended to neighbouring regions that came under French military conquest. Including those years would mix an exceptional political event with the structural, long-run trends the study is designed to examine, so the authors exclude them.
Main patterns in the data
The Little Divergence between north and south
The most prominent result is a divergence that the authors call the “Little Divergence.” From around 1500, average human capital per university rose in the English Realms, Evangelical Germania, and the Netherlands. Over the same period, the series for Occitania, Central Italy, Portugal, and Castilla stagnated. The authors frame this as a pattern in the data. It is not presented as proof of a single cause.
Academies change the picture in France
Comparing universities alone can understate scholarly activity in some places. In the authors’ discussion of Francia and Occitania, adding academies widens the gap between the two. Parisian academies make a substantial contribution to Francia’s series. A university-only comparison therefore gives a narrower view of how far the regions diverged.
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The Black Death, the Thirty Years’ War, and religion
The authors also examine patterns around the Black Death and the Thirty Years’ War, and they compare Catholic and Protestant areas of the Holy Roman Empire. These are descriptive comparisons. The study shows how the series behave around these events, and it does not establish that any single event caused a change in academic human capital.
Scotland and the Scottish Enlightenment
Scotland follows a distinct trajectory in the series, and the authors discuss it in relation to the Scottish Enlightenment. Readers should treat this as a regional case study within the dataset rather than as an independent test of Enlightenment causation.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAcademic human capital is not literacy
The study separates academic human capital from literacy and book consumption. The authors note that the Netherlands could rank especially high on literacy, while the English Realms led the academic-human-capital measure. The two indicators capture different things, and a region can lead on one and be middling on the other.
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Reading the series without overstating them
Several checks prevent common misreadings:
- Confirm the aggregation level before comparing figures. City, present-day country, and macro-region values are not interchangeable.
- Separate totals from per-university values. A region with many weak institutions can match one with few strong ones on totals while differing sharply per institution.
- Decide whether academy-only members are included. Series that drop them show a smaller academic population in places such as Paris.
- Do not substitute literacy, book production, or general schooling for the index. They are different measures and can move in different directions.
- Keep religious and political labels attached to their historical boundaries and dates, especially for the Holy Roman Empire.
Related context on scholar mobility
A separate peer-reviewed study, published in the Journal of the European Economic Association in 2024, examines the medieval and early modern academic market from 1000 to 1800. It uses a database of about 48,000 scholars and reports that talent concentrated at stronger universities, that better scholars tended to sort toward more attractive institutions, and that they were more mobile. That study has its own database and method. It helps explain how academic talent moved and clustered, but it does not validate each result in the 2026 series.
Data access and versions
The 2026 article is open access, and the authors list the data and replication package under DOI 10.3886/E247226V1. Anyone using the series should cite the journal article for publication. When discussing technical details of the aggregation method, identify whether the 2025 working paper or the 2026 article is the source, since the two versions are not identical in every detail.
In the authors’ own words
The authors summarise their contribution this way: “We have presented new data on the evolution of academic human capital in preindustrial Europe, based on systematic individual-level data and historically-grounded regional classifications.”
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThat summary matches the scope described above. The series give a detailed, individual-based view of a documented scholarly elite across six centuries, and their value lies in the comparisons they make possible within that scope.
Use the study for questions about where academic talent and institutions were concentrated and how that changed over time. For questions about mass literacy, schooling, or the everyday skills of ordinary Europeans, look to other evidence and treat this series as one input among several.
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